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1) Loading data
Downloading...
 From (original): https://drive.google.com/uc?id=1gb2vWn0V2unstElGTTrHIIWIftHbXJvz
-From (redirected): https://drive.google.com/uc?id=1gb2vWn0V2unstElGTTrHIIWIftHbXJvz&confirm=t&uuid=fe3c29ed-844d-4549-bcbe-cfcbbb5c4636
+From (redirected): https://drive.google.com/uc?id=1gb2vWn0V2unstElGTTrHIIWIftHbXJvz&confirm=t&uuid=31584bd7-c32c-4a78-a561-6bc179117eca
 To: /home/runner/work/fornax-demo-notebooks/fornax-demo-notebooks/light_curves/data/df_lc_020724.parquet.gzip
-100%|██████████| 243M/243M [00:05<00:00, 45.9MB/s]
+100%|██████████| 243M/243M [00:03<00:00, 73.7MB/s]
 
@@ -843,7 +843,7 @@

2) Preprocess data for ML (ZTF bands) -../_images/479c834d7125c3ab0a27ac62468bddd2f136962360746cb5e9a79195807c2788.png +../_images/479c834d7125c3ab0a27ac62468bddd2f136962360746cb5e9a79195807c2788.png ../_images/0839d2412a65c3e367c0f6c3cc71e75ebc54d68141fbb1eef020d21500fdd865.png ../_images/3bbc0a6741df42fbdf54d635788ee3a19ffcc0a0335e67b949829d10f08b4290.png ../_images/70d583035f9462b8e7c10836f1891d07edf799e8b53bb1aebc78ea238fa97833.png @@ -877,7 +877,7 @@

2) Preprocess data for ML (ZTF bands) -../_images/51370219c97be25bde7004d24240de083213b00162fc4ea4a25b88b96b6b0094.png +../_images/c1f5f4232c9752769f18c7b4e0fc6183532ff020d8114bf9de23f1adb5582bda.png @@ -932,7 +932,7 @@

3) Learn the Manifold -../_images/f0854eaa1d9381f32cd50bc9a13ba60b635b75a5e611199c9aa05ff667a8c604.png +../_images/6296b6e6e64be805edea7fce7179b2d36c1596cf61c098c631d88b7f34083874.png

The left panel is colorcoded by the origin of the sample. The middle panel shows the sum of mean brightnesses in three bands (arbitrary unit) demonstrating that after normalization we see no correlation with brightness. The panel on the right is color coded by a statistical measure of variability (i.e. the fractional variation see here). As with the plotting above it is not easy to see all the data points and correlations in the next two cells measure probability of belonging to each original sample as well as the mean statistical property on an interpolated grid on this reduced 2D projected surface.

@@ -983,10 +983,10 @@

3) Learn the Manifold
-
<matplotlib.colorbar.Colorbar at 0x7f82614945d0>
+
<matplotlib.colorbar.Colorbar at 0x7fcca8112f50>
 
-../_images/4b3662ca77e7e25fa5257fd654ca52662bd73887bc8adf6eeb2988737d5bcb61.png +../_images/2b52807c6d85264f2cad63028111b8c9879abfe5e3a2fadb33765c44ab156a02.png
@@ -1018,11 +1018,11 @@

3.1) Sample comparison on the UMAP -
/tmp/ipykernel_1899/3793995111.py:10: RuntimeWarning: invalid value encountered in divide
+
/tmp/ipykernel_1931/3793995111.py:10: RuntimeWarning: invalid value encountered in divide
   prob = hist_per_cluster / hist
 
-../_images/8505ccadb26b53769c65c5b60a21bb9c24d46e4a02b209a2490be6c54b7247cd.png +../_images/2df30feaa464ab8ac60a164b82ab428cc75f3c7f58e9a5c61d308942f104c9ff.png

Figure above shows how with ZTF light curves alone we can separate some of these AGN samples, where they have overlaps. We can do a similar exercise with other dimensionality reduction techniques. Below we show two SOMs one with normalized and another with no normalization. The advantage of Umaps to SOMs is that in practice you may change the parameters to separate classes of vastly different data points, as distance is preserved on a umap. On a SOM however only topology of higher dimensions is preserved and not distance hence, the change on the 2d grid does not need to be smooth and from one cell to next there might be larg jumps. On the other hand, an advantage of the SOM is that by definition it has a grid and no need for a posterior interpolation (as we did above) is needed to map more data or to measure probabilities, etc.

@@ -1040,24 +1040,24 @@

3.2) Reduced dimensions on a SOM grid
Training...
-pca_linear_initialization took: 0.013000 seconds
+pca_linear_initialization took: 0.014000 seconds
 Rough training...
 radius_ini: 2.000000 , radius_final: 1.000000, trainlen: 5
 epoch: 1 ---> elapsed time:  0.042000, quantization error: 7.891746
-epoch: 2 ---> elapsed time:  0.034000, quantization error: 7.278950
-epoch: 3 ---> elapsed time:  0.023000, quantization error: 7.065468
-epoch: 4 ---> elapsed time:  0.016000, quantization error: 6.941469
-epoch: 5 ---> elapsed time:  0.015000, quantization error: 6.804933
+epoch: 2 ---> elapsed time:  0.047000, quantization error: 7.278950
+epoch: 3 ---> elapsed time:  0.017000, quantization error: 7.065468
+epoch: 4 ---> elapsed time:  0.017000, quantization error: 6.941469
+epoch: 5 ---> elapsed time:  0.017000, quantization error: 6.804933
 Finetune training...
 radius_ini: 1.000000 , radius_final: 1.000000, trainlen: 6
 epoch: 1 ---> elapsed time:  0.017000, quantization error: 6.635077
 epoch: 2 ---> elapsed time:  0.015000, quantization error: 6.586020
-epoch: 3 ---> elapsed time:  0.015000, quantization error: 6.569531
-epoch: 4 ---> elapsed time:  0.014000, quantization error: 6.555973
-epoch: 5 ---> elapsed time:  0.016000, quantization error: 6.548818
-epoch: 6 ---> elapsed time:  0.017000, quantization error: 6.545620
+epoch: 3 ---> elapsed time:  0.017000, quantization error: 6.569531
+epoch: 4 ---> elapsed time:  0.016000, quantization error: 6.555973
+epoch: 5 ---> elapsed time:  0.017000, quantization error: 6.548818
+epoch: 6 ---> elapsed time:  0.015000, quantization error: 6.545620
 Final quantization error: 6.543132
-train took: 0.299000 seconds
+train took: 0.310000 seconds
 

@@ -1289,24 +1289,24 @@

3.2) Reduced dimensions on a SOM grid
Training...
-pca_linear_initialization took: 0.079000 seconds
+pca_linear_initialization took: 0.069000 seconds
 Rough training...
 radius_ini: 2.000000 , radius_final: 1.000000, trainlen: 5
-epoch: 1 ---> elapsed time:  0.288000, quantization error: 5.114529
-epoch: 2 ---> elapsed time:  0.016000, quantization error: 2.607937
-epoch: 3 ---> elapsed time:  0.016000, quantization error: 1.832338
-epoch: 4 ---> elapsed time:  0.015000, quantization error: 1.587327
-epoch: 5 ---> elapsed time:  0.015000, quantization error: 1.480731
+epoch: 1 ---> elapsed time:  0.036000, quantization error: 5.114529
+epoch: 2 ---> elapsed time:  0.034000, quantization error: 2.607937
+epoch: 3 ---> elapsed time:  0.027000, quantization error: 1.832338
+epoch: 4 ---> elapsed time:  0.017000, quantization error: 1.587327
+epoch: 5 ---> elapsed time:  0.024000, quantization error: 1.480731
 Finetune training...
 radius_ini: 1.000000 , radius_final: 1.000000, trainlen: 6
 epoch: 1 ---> elapsed time:  0.015000, quantization error: 1.405660
-epoch: 2 ---> elapsed time:  0.015000, quantization error: 1.394511
-epoch: 3 ---> elapsed time:  0.015000, quantization error: 1.391247
-epoch: 4 ---> elapsed time:  0.015000, quantization error: 1.385959
+epoch: 2 ---> elapsed time:  0.016000, quantization error: 1.394511
+epoch: 3 ---> elapsed time:  0.016000, quantization error: 1.391247
+epoch: 4 ---> elapsed time:  0.017000, quantization error: 1.385959
 epoch: 5 ---> elapsed time:  0.016000, quantization error: 1.381555
 epoch: 6 ---> elapsed time:  0.016000, quantization error: 1.380592
 Final quantization error: 1.377762
-train took: 0.572000 seconds
+train took: 0.358000 seconds
 
@@ -1437,7 +1437,7 @@

4) Repeating the above, this time with ZTF + WISE manifold -../_images/a1e87edde41a00bf060b28dffb5c946b34c128db189a7dd821d1bf9c53848ded.png +../_images/a1e87edde41a00bf060b28dffb5c946b34c128db189a7dd821d1bf9c53848ded.png ../_images/0441337b249d3071451619e583a7cef4ba13a446a4102febcc9ac5b07c8bac54.png ../_images/96ea2813f29d5a5fb8c05829926aa88e9f2be06cccb81e830c93d46f4b0a6c52.png -
(0.5733959794044494,
- 13.698460566997529,
- -0.6840989537537099,
- 9.728761116415262)
+
(-1.380801185965538,
+ 12.374004408717155,
+ 0.7047346293926239,
+ 10.856207901239395)
 
-../_images/85d2ee85ba76a010406657b891ad498dfba64a8c50988a15005f24d2c42d1140.png +../_images/374f03f671f8c369943cb3325a239eb3159c3288e2adc413fe005a01d44e3790.png
diff --git a/light_curves/light_curve_classifier.html b/light_curves/light_curve_classifier.html index bcfa1107..e531e137 100644 --- a/light_curves/light_curve_classifier.html +++ b/light_curves/light_curve_classifier.html @@ -1698,12 +1698,12 @@

4.1 A single Classifier
-
Accuracy of Random Interval Classifier: 0.7111111111111111
-CPU times: user 2.09 s, sys: 1.75 s, total: 3.84 s
+
Accuracy of Random Interval Classifier: 0.7333333333333333
+CPU times: user 2.18 s, sys: 1.79 s, total: 3.96 s
 Wall time: 1min 16s
 
-../_images/e605f2326d3cb648fb9ebdf0c035a57d87422354e535cd6bc96b9a61a863e7a4.png +../_images/fdd83308433c157730fefb6507563e4982725d63fc5f6fb15f6cb5e9e2fe68d4.png
@@ -2147,6 +2147,14 @@

5.0 Create a candidate list - + @@ -801,59 +801,59 @@

top - 2024-03-01 21:05:37+00:00 - 0.92 - 0.90 - 0.91 + 2024-03-01 23:04:43+00:00 + 1.73 + 2.08 + 2.24 61.8 - 46.3 - 7.2 - 54.0 - 0 days 00:34:09 + 34.2 + 14.0 + 47.1 + 0 days 02:33:15 - 2024-03-01 23:00:17+00:00 - 2.70 - 2.57 - 2.41 + 2024-03-01 23:06:32+00:00 + 1.40 + 1.87 + 2.14 61.8 - 33.3 - 19.5 - 41.9 - 0 days 02:28:49 + 17.4 + 30.8 + 30.4 + 0 days 02:35:04 - 2024-03-01 20:31:28+00:00 - 0.12 - 0.03 - 0.15 + 2024-03-01 21:05:05+00:00 + 0.92 + 0.89 + 0.91 61.8 - 52.2 - 1.3 - 59.8 - 0 days 00:00:00 + 46.2 + 7.1 + 54.0 + 0 days 00:33:37 - 2024-03-01 20:42:16+00:00 - 1.79 - 1.56 - 0.91 + 2024-03-01 22:57:00+00:00 + 2.76 + 2.55 + 2.38 61.8 - 48.5 - 4.9 - 56.3 - 0 days 00:10:48 + 32.7 + 20.1 + 41.9 + 0 days 02:25:32 - 2024-03-01 21:22:44+00:00 - 1.69 - 0.87 - 0.81 + 2024-03-01 22:18:53+00:00 + 2.62 + 2.30 + 2.29 61.8 - 45.5 - 7.4 - 53.9 - 0 days 00:51:16 + 33.0 + 19.9 + 41.9 + 0 days 01:47:25 @@ -922,94 +922,94 @@

top - 2024-03-01 23:01:58+00:00 + 2024-03-01 21:23:57+00:00 ztf - ztf - 3984 + ztf-worker + 13392 jovyan 20 0 - 37.9 - 9.9 - 0.1 - R - 100.0 - 16.0 - 2:59.09 + 4.8 + 1.5 + 0.0 + S + 6.7 + 2.4 + 0:25.60 python - 0 days 02:30:30 + 0 days 00:52:29 - 2024-03-01 22:57:30+00:00 + 2024-03-01 20:43:32+00:00 ztf ztf 3984 jovyan 20 0 - 12.6 - 9.6 + 2.9 + 0.3 0.1 S 0.0 - 15.5 - 1:51.91 + 0.5 + 0:09.00 python - 0 days 02:26:02 + 0 days 00:12:04 - 2024-03-01 21:30:25+00:00 - ztf + 2024-03-01 21:27:38+00:00 ztf - 3984 + ztf-worker + 13392 jovyan 20 0 - 4.6 - 1.8 - 0.1 + 5.3 + 1.6 + 0.0 S 0.0 - 2.9 - 1:01.24 + 2.5 + 1:09.83 python - 0 days 00:58:57 + 0 days 00:56:10 - 2024-03-01 21:27:54+00:00 + 2024-03-01 23:00:17+00:00 ztf ztf 3984 jovyan 20 0 - 4.5 - 1.7 + 12.7 + 9.7 0.1 S 0.0 - 2.7 - 1:00.28 + 15.8 + 1:55.46 python - 0 days 00:56:26 + 0 days 02:28:49 - 2024-03-01 22:09:51+00:00 + 2024-03-01 21:57:49+00:00 ztf ztf-worker - 13392 + 13394 jovyan 20 0 - 7.8 - 2.0 + 7.4 + 1.3 0.0 S - 20.0 - 3.3 - 10:27.65 + 40.0 + 2.2 + 7:38.34 python - 0 days 01:38:23 + 0 days 01:26:21 @@ -1066,7 +1066,7 @@

top

-
2024/09/27 20:52:36 UTC | starting explode
+
2024/09/30 20:54:54 UTC | starting explode
 
@@ -1206,20 +1206,20 @@

Example 2: Parallelizing the light_curve_generator notebook -
2024/09/27 20:52:37 UTC | [pid=2281] Starting build=sample
+
2024/09/30 20:54:54 UTC | [pid=2318] Starting build=sample
 Building object sample from: ['yang']
 Changing Look AGN- Yang et al:  31
 Object sample size, after duplicates removal: 30
 Object sample saved to: /home/runner/work/fornax-demo-notebooks/fornax-demo-notebooks/light_curves/output/lightcurves-demo-Yang-sample/object_sample.ecsv
-2024/09/27 20:52:39 UTC
-2024/09/27 20:52:39 UTC | [pid=2330] Starting build=lightcurves, archive=hcv
-2024/09/27 20:52:39 UTC | [pid=2329] Starting build=lightcurves, archive=gaia
-2024/09/27 20:52:39 UTC | [pid=2331] Starting build=lightcurves, archive=heasarc
-2024/09/27 20:52:39 UTC | [pid=2334] Starting build=lightcurves, archive=tess_kepler
-2024/09/27 20:52:39 UTC | [pid=2332] Starting build=lightcurves, archive=icecube
-2024/09/27 20:52:39 UTC | [pid=2335] Starting build=lightcurves, archive=wise
-2024/09/27 20:52:39 UTC | [pid=2333] Starting build=lightcurves, archive=panstarrs
-2024/09/27 20:52:39 UTC | [pid=2336] Starting build=lightcurves, archive=ztf
+2024/09/30 20:54:56 UTC
+2024/09/30 20:54:56 UTC | [pid=2365] Starting build=lightcurves, archive=gaia
+2024/09/30 20:54:56 UTC | [pid=2367] Starting build=lightcurves, archive=heasarc
+2024/09/30 20:54:56 UTC | [pid=2366] Starting build=lightcurves, archive=hcv
+2024/09/30 20:54:56 UTC | [pid=2368] Starting build=lightcurves, archive=icecube
+2024/09/30 20:54:56 UTC | [pid=2370] Starting build=lightcurves, archive=tess_kepler
+2024/09/30 20:54:56 UTC | [pid=2372] Starting build=lightcurves, archive=ztf
+2024/09/30 20:54:56 UTC | [pid=2371] Starting build=lightcurves, archive=wise
+2024/09/30 20:54:56 UTC | [pid=2369] Starting build=lightcurves, archive=panstarrs
 working on mission
 FERMIGTRIG
 working on mission
@@ -1228,33 +1228,33 @@ 

Example 2: Parallelizing the light_curve_generator notebook +

The light curves are saved in the “parquet_dir” directory. Each archive call writes data to a parquet file in its own subdirectory. @@ -1370,93 +1370,95 @@

Example 2: Parallelizing the light_curve_generator notebook29 - Yang 18 - BP - 56959.897476 - 0.145461 - 0.004619 - gaia - - - panstarrs r - 55352.400562 - 0.073599 - 0.001258 + 17 + Yang 18 + panstarrs i + 56794.373880 + 0.502129 + 0.003863 panstarrs - 8 + 14 Yang 18 - panstarrs y - 56408.279660 - 0.310015 - 0.009319 - panstarrs + G + 57739.112160 + 0.037169 + 0.001764 + gaia - 13 + 5 Yang 18 - panstarrs g - 55983.506415 - 0.013669 - 0.001218 - panstarrs + BP + 57290.020150 + 0.216419 + 0.005135 + gaia - 21 + 20 Yang 18 - RP - 57281.807777 - 0.480648 - 0.010094 + BP + 56900.853961 + 0.108195 + 0.007329 gaia + + 6 + Yang 18 + panstarrs y + 56405.252664 + 0.123749 + 0.009207 + panstarrs +

@@ -1714,7 +1716,7 @@

Using a yaml file -
2024/09/27 20:55:15 UTC | [pid=2281] Starting build=sample
+
2024/09/30 20:58:38 UTC | [pid=2318] Starting build=sample
 Building object sample from: ['sdss', 'ztf_objectid', 'green', 'papers_list', 'ruan']
 SDSS Quasar: 10
 number of ztf coords added by Objectname: 4
@@ -1724,7 +1726,7 @@ 

Using a yaml file
WARNING: OverflowError converting to IntType in column specObjID, reverting to String. [astropy.io.ascii.fastbasic]
diff --git a/searchindex.js b/searchindex.js
index e2ce2e35..5e48129c 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"1) Loading data": [[4, "loading-data"]], "1. Define the sample": [[7, "define-the-sample"]], "1. Get an account on the Fornax Science Console": [[0, "get-an-account-on-the-fornax-science-console"]], "1. Read in a dataset of archival light curves": [[6, "read-in-a-dataset-of-archival-light-curves"]], "1. Retrieve Initial Catalog from IRSA": [[2, "retrieve-initial-catalog-from-irsa"]], "1.1 Build your own sample": [[7, "build-your-own-sample"]], "1.1) What is in this sample": [[4, "what-is-in-this-sample"]], "1.2 Write out your sample to disk": [[7, "write-out-your-sample-to-disk"]], "1.3 Load the sample table from disk": [[7, "load-the-sample-table-from-disk"]], "1.4 Initialize data structure to hold the light curves": [[7, "initialize-data-structure-to-hold-the-light-curves"]], "1a. Filter Catalog": [[2, "a-filter-catalog"]], "2) Preprocess data for ML (ZTF bands)": [[4, "preprocess-data-for-ml-ztf-bands"]], "2. Data Prep": [[6, "data-prep"]], "2. Find light curves for these targets in NASA catalogs": [[7, "find-light-curves-for-these-targets-in-nasa-catalogs"]], "2. Log into the Fornax Science Console": [[0, "log-into-the-fornax-science-console"]], "2. Retrieve Image Datasets from the Cloud": [[2, "retrieve-image-datasets-from-the-cloud"]], "2.1 HEASARC: FERMI & Beppo SAX": [[7, "heasarc-fermi-beppo-sax"]], "2.1 Remove bands with not enough data": [[6, "remove-bands-with-not-enough-data"]], "2.2 Combine Labels for a Simpler Classification": [[6, "combine-labels-for-a-simpler-classification"]], "2.2 IRSA: ZTF": [[7, "irsa-ztf"]], "2.3 Data Visualization": [[6, "data-visualization"]], "2.3 IRSA: WISE": [[7, "irsa-wise"]], "2.4 Clean the dataset of unwanted data": [[6, "clean-the-dataset-of-unwanted-data"]], "2.4 MAST: Pan-STARRS": [[7, "mast-pan-starrs"]], "2.5 MAST: TESS, Kepler and K2": [[7, "mast-tess-kepler-and-k2"]], "2.5 Missing Data": [[6, "missing-data"]], "2.6  Make all objects and bands have identical time arrays (uniform length and spacing)": [[6, "make-all-objects-and-bands-have-identical-time-arrays-uniform-length-and-spacing"]], "2.6 MAST: Hubble Catalog of Variables (HCV)": [[7, "mast-hubble-catalog-of-variables-hcv"]], "2.7  Restructure dataframe": [[6, "restructure-dataframe"]], "2.8 Normalize": [[6, "normalize"]], "2.9 Cleanup": [[6, "cleanup"]], "3) Learn the Manifold": [[4, "learn-the-manifold"]], "3.  Run Forced Photometry": [[2, "run-forced-photometry"]], "3. Find light curves for these targets in relevant, non-NASA catalogs": [[7, "find-light-curves-for-these-targets-in-relevant-non-nasa-catalogs"]], "3. Prep for ML algorithms": [[6, "prep-for-ml-algorithms"]], "3. Start a new notebook.": [[0, "start-a-new-notebook"]], "3.1 Gaia": [[7, "gaia"]], "3.1 Train test split": [[6, "train-test-split"]], "3.1) Sample comparison on the UMAP": [[4, "sample-comparison-on-the-umap"]], "3.2) Reduced dimensions on a SOM grid": [[4, "reduced-dimensions-on-a-som-grid"]], "3.3 IceCube neutrinos": [[7, "icecube-neutrinos"]], "3a. Setup": [[2, "a-setup"]], "3b. Main Function to do the Forced Photometry": [[2, "b-main-function-to-do-the-forced-photometry"]], "3c. Calculate Forced Photometry with Straightforward but Slow Method": [[2, "c-calculate-forced-photometry-with-straightforward-but-slow-method"]], "3d. Calculate Forced Photometry - Parallelization": [[2, "d-calculate-forced-photometry-parallelization"]], "3e. Cleanup": [[2, "e-cleanup"]], "3f. Plot to Confirm our Photometry Results": [[2, "f-plot-to-confirm-our-photometry-results"]], "4) Repeating the above, this time with ZTF + WISE manifold": [[4, "repeating-the-above-this-time-with-ztf-wise-manifold"]], "4. Cross Match our New Photometry Catalog with an X-ray archival Catalog": [[2, "cross-match-our-new-photometry-catalog-with-an-x-ray-archival-catalog"]], "4. End your JupyterHub session.": [[0, "end-your-jupyterhub-session"]], "4. Parallel processing the archive calls": [[7, "parallel-processing-the-archive-calls"]], "4. Run sktime algorithms on the light curves": [[6, "run-sktime-algorithms-on-the-light-curves"]], "4.1 A single Classifier": [[6, "a-single-classifier"]], "4.1 Check that the data types are ok for sktime": [[6, "check-that-the-data-types-are-ok-for-sktime"]], "4.2 Loop over a bunch of classifiers": [[6, "loop-over-a-bunch-of-classifiers"]], "4a. Retrieve the HEASARC Catalog": [[2, "a-retrieve-the-heasarc-catalog"]], "4b. Run nway to do the Cross-Match": [[2, "b-run-nway-to-do-the-cross-match"]], "5) Wise bands alone": [[4, "wise-bands-alone"]], "5. Make plots of luminosity as a function of time": [[7, "make-plots-of-luminosity-as-a-function-of-time"]], "5. Plot Final Results": [[2, "plot-final-results"]], "5.0 Create a candidate list": [[6, "create-a-candidate-list"]], "6) UMAP with different metrics/distances on ZTF+WISE": [[4, "umap-with-different-metrics-distances-on-ztf-wise"]], "AGN Zoo: Comparison of AGN selected with different metrics": [[4, null]], "About this Notebook": [[4, "about-this-notebook"]], "About this notebook": [[8, "about-this-notebook"]], "Acknowledgements": [[6, "acknowledgements"]], "Acknowledgements:": [[2, "acknowledgements"], [7, "acknowledgements"]], "Additional Resources": [[0, "additional-resources"]], "Advice for the user": [[8, "advice-for-the-user"]], "Appendix: What to expect": [[8, "appendix-what-to-expect"]], "Authors": [[6, "authors"]], "Authors:": [[2, "authors"], [7, "authors"]], "Automated Multiband Forced Photometry on Large Datasets": [[2, null]], "Bash script flags": [[8, "bash-script-flags"]], "Can I install my own software on the Fornax Science Console?": [[0, "can-i-install-my-own-software-on-the-fornax-science-console"]], "Cancel": [[8, "cancel"]], "Challenges of large-scale runs": [[8, "challenges-of-large-scale-runs"]], "Citations": [[4, "citations"]], "Cloud": [[0, "cloud"]], "Conclusions": [[6, "conclusions"]], "Cross matching two large catalogs": [[0, "cross-matching-two-large-catalogs"]], "Data Management": [[0, "data-management"]], "Data access within the Fornax Science Console": [[0, "data-access-within-the-fornax-science-console"]], "Distributed processing with Dask DataFrames \u2013 basic concepts": [[0, "distributed-processing-with-dask-dataframes-basic-concepts"]], "Does work persist between sessions?": [[0, "does-work-persist-between-sessions"]], "Example 1: Multi-wavelength light curves for 500,000 SDSS AGN": [[8, "example-1-multi-wavelength-light-curves-for-500-000-sdss-agn"]], "Example 2: Parallelizing the light_curve_generator notebook": [[8, "example-2-parallelizing-the-light-curve-generator-notebook"]], "Example 3: Keyword arguments and script flags": [[8, "example-3-keyword-arguments-and-script-flags"]], "Examples and Tutorials": [[0, "examples-and-tutorials"]], "Fornax Science Console": [[0, null], [8, "fornax-science-console"]], "Fornax Science Console basic capabilities and selection guidelines": [[0, "fornax-science-console-basic-capabilities-and-selection-guidelines"]], "Fully worked science use cases": [[0, "fully-worked-science-use-cases"]], "Getting Help": [[0, "getting-help"]], "HEASARC sciserver_cookbooks": [[0, "heasarc-sciserver-cookbooks"]], "How can I access data that has been hosted on the cloud by the Fornax archives?": [[0, "how-can-i-access-data-that-has-been-hosted-on-the-cloud-by-the-fornax-archives"]], "How can I contribute to existing Open-Source Fornax notebook tutorials?": [[0, "how-can-i-contribute-to-existing-open-source-fornax-notebook-tutorials"]], "How can I get a list of what software is pre-installed on the Fornax Science Console?": [[0, "how-can-i-get-a-list-of-what-software-is-pre-installed-on-the-fornax-science-console"]], "How can I open a plot (e.g. png, pdf) that I generated in a notebook or uploaded?": [[0, "how-can-i-open-a-plot-e-g-png-pdf-that-i-generated-in-a-notebook-or-uploaded"]], "How can I run a notebook non-interactively?": [[0, "how-can-i-run-a-notebook-non-interactively"]], "How can I save my notebook as a Python script?": [[0, "how-can-i-save-my-notebook-as-a-python-script"]], "How can I tell if I am close to using up my allocation of compute and storage resources?": [[0, "how-can-i-tell-if-i-am-close-to-using-up-my-allocation-of-compute-and-storage-resources"]], "How can I tell which computing resources are available on the Fornax Science Console?": [[0, "how-can-i-tell-which-computing-resources-are-available-on-the-fornax-science-console"]], "How can I upload my own data for use with compute provided by the Fornax Science Console?": [[0, "how-can-i-upload-my-own-data-for-use-with-compute-provided-by-the-fornax-science-console"]], "How do I download data from the Fornax Science Console to my local machine?": [[0, "how-do-i-download-data-from-the-fornax-science-console-to-my-local-machine"]], "How do I share data from inside Fornax with collaborators?": [[0, "how-do-i-share-data-from-inside-fornax-with-collaborators"]], "How will my analysis be affected by memory limitations?": [[0, "how-will-my-analysis-be-affected-by-memory-limitations"]], "Imports": [[2, "imports"], [4, "imports"], [6, "imports"], [8, "imports"]], "Imports:": [[7, "imports"]], "Input": [[6, "input"]], "Input:": [[2, "input"], [7, "input"]], "Interpreting top": [[8, "interpreting-top"]], "Introduction": [[4, "introduction"], [6, "introduction"], [8, "introduction"]], "Introduction:": [[2, "introduction"], [7, "introduction"]], "Is it possible to bring my own docker image?": [[0, "is-it-possible-to-bring-my-own-docker-image"]], "Is it possible to do code development in emacs or vi or some other IDE?": [[0, "is-it-possible-to-do-code-development-in-emacs-or-vi-or-some-other-ide"]], "Is it possible to launch apps from icons? Like MOPEX or SPICE": [[0, "is-it-possible-to-launch-apps-from-icons-like-mopex-or-spice"]], "Is it possible to run licensed software (IDL) in Fornax?": [[0, "is-it-possible-to-run-licensed-software-idl-in-fornax"]], "Is it possible to run the container from Fornax on my own personal computer/laptop?": [[0, "is-it-possible-to-run-the-container-from-fornax-on-my-own-personal-computer-laptop"]], "Is there a way access data in a Box account from the Fornax Science Console?": [[0, "is-there-a-way-access-data-in-a-box-account-from-the-fornax-science-console"]], "Is there a way to access data from an AWS bucket?": [[0, "is-there-a-way-to-access-data-from-an-aws-bucket"]], "Launch the run": [[8, "launch-the-run"]], "Learning Goals": [[4, "learning-goals"], [6, "learning-goals"], [7, "learning-goals"], [8, "learning-goals"]], "Learning Goals:": [[2, "learning-goals"]], "Light Curve Classifier": [[6, null]], "Logs": [[8, "logs"]], "MAST science examples": [[0, "mast-science-examples"]], "Make Multi-Wavelength Light Curves Using Archival Data": [[7, null]], "Make Multi-Wavelength Light Curves for Large Samples": [[8, null]], "Managing Software": [[0, "managing-software"]], "Monitor": [[8, "monitor"]], "Multi-band forced photometry": [[1, null]], "Navigating JupyterLab in the Fornax Science Console": [[0, "navigating-jupyterlab-in-the-fornax-science-console"]], "Needs and wants for large-scale runs": [[8, "needs-and-wants-for-large-scale-runs"]], "Non-standard Dependencies": [[2, "non-standard-dependencies"]], "Optimizing code for CPU usage (CPU profiling)": [[0, "optimizing-code-for-cpu-usage-cpu-profiling"]], "Optimizing code for memory usage (memory profiling)": [[0, "optimizing-code-for-memory-usage-memory-profiling"]], "Optimizing code for multiple CPUs with parallelization": [[0, "optimizing-code-for-multiple-cpus-with-parallelization"]], "Output": [[6, "output"]], "Output:": [[2, "output"], [7, "output"]], "Overview": [[8, "overview"]], "Parallel and Distributed Processing": [[0, "parallel-and-distributed-processing"]], "Parallel processing methods: bash script vs. python\u2019s multiprocessing": [[8, "parallel-processing-methods-bash-script-vs-python-s-multiprocessing"]], "Python kwargs_dict": [[8, "python-kwargs-dict"]], "Quick Start Guide": [[0, "quick-start-guide"]], "References": [[2, "references"], [7, "references"]], "References:": [[6, "references"]], "Restart": [[8, "restart"]], "Runtime": [[4, "runtime"], [6, "runtime"]], "Save your work!": [[0, "save-your-work"]], "Starting & Monitoring Analyses": [[0, "starting-monitoring-analyses"]], "Terminology": [[0, "terminology"]], "The Fornax Initiative": [[3, null]], "The bash script": [[8, "the-bash-script"]], "The python helper": [[8, "the-python-helper"]], "Time Domain": [[5, null]], "Troubleshooting": [[0, "troubleshooting"]], "Tutorial Notebooks": [[3, "tutorial-notebooks"], [3, null]], "Use IVOA image search and Fornax download to obtain Galex from the MAST archive": [[2, "use-ivoa-image-search-and-fornax-download-to-obtain-galex-from-the-mast-archive"]], "Use the fornax cloud access API to obtain the IRAC data from the IRSA S3 bucket.": [[2, "use-the-fornax-cloud-access-api-to-obtain-the-irac-data-from-the-irsa-s3-bucket"]], "User Documentation": [[3, "user-documentation"], [3, null]], "Using a yaml file": [[8, "using-a-yaml-file"]], "What is a kernel and how do I choose one?": [[0, "what-is-a-kernel-and-how-do-i-choose-one"]], "What is the Fornax Science Console?": [[0, "what-is-the-fornax-science-console"]], "What is the terminal command to list package version info using pip?": [[0, "what-is-the-terminal-command-to-list-package-version-info-using-pip"]], "When should I use distributed or parallel processing?": [[0, "when-should-i-use-distributed-or-parallel-processing"]], "Will notebooks that run on Fornax also work on my laptop?": [[0, "will-notebooks-that-run-on-fornax-also-work-on-my-laptop"]], "Work with theoretical catalogs": [[0, "work-with-theoretical-catalogs"]], "top": [[8, "top"]]}, "docnames": ["documentation/README", "forced_photometry/README", "forced_photometry/multiband_photometry", "index", "light_curves/ML_AGNzoo", "light_curves/README", "light_curves/light_curve_classifier", "light_curves/light_curve_generator", "light_curves/scale_up"], "envversion": {"sphinx": 63, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2}, "filenames": ["documentation/README.md", "forced_photometry/README.md", "forced_photometry/multiband_photometry.md", "index.md", "light_curves/ML_AGNzoo.md", "light_curves/README.md", "light_curves/light_curve_classifier.md", "light_curves/light_curve_generator.md", "light_curves/scale_up.md"], "indexentries": {}, "objects": {}, "objnames": {}, "objtypes": {}, "terms": 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+Search.setIndex({"alltitles": {"1) Loading data": [[4, "loading-data"]], "1. Define the sample": [[7, "define-the-sample"]], "1. Get an account on the Fornax Science Console": [[0, "get-an-account-on-the-fornax-science-console"]], "1. Read in a dataset of archival light curves": [[6, "read-in-a-dataset-of-archival-light-curves"]], "1. Retrieve Initial Catalog from IRSA": [[2, "retrieve-initial-catalog-from-irsa"]], "1.1 Build your own sample": [[7, "build-your-own-sample"]], "1.1) What is in this sample": [[4, "what-is-in-this-sample"]], "1.2 Write out your sample to disk": [[7, "write-out-your-sample-to-disk"]], "1.3 Load the sample table from disk": [[7, "load-the-sample-table-from-disk"]], "1.4 Initialize data structure to hold the light curves": [[7, "initialize-data-structure-to-hold-the-light-curves"]], "1a. Filter Catalog": [[2, "a-filter-catalog"]], "2) Preprocess data for ML (ZTF bands)": [[4, "preprocess-data-for-ml-ztf-bands"]], "2. Data Prep": [[6, "data-prep"]], "2. 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Calculate Forced Photometry - Parallelization": [[2, "d-calculate-forced-photometry-parallelization"]], "3e. Cleanup": [[2, "e-cleanup"]], "3f. Plot to Confirm our Photometry Results": [[2, "f-plot-to-confirm-our-photometry-results"]], "4) Repeating the above, this time with ZTF + WISE manifold": [[4, "repeating-the-above-this-time-with-ztf-wise-manifold"]], "4. Cross Match our New Photometry Catalog with an X-ray archival Catalog": [[2, "cross-match-our-new-photometry-catalog-with-an-x-ray-archival-catalog"]], "4. End your JupyterHub session.": [[0, "end-your-jupyterhub-session"]], "4. Parallel processing the archive calls": [[7, "parallel-processing-the-archive-calls"]], "4. Run sktime algorithms on the light curves": [[6, "run-sktime-algorithms-on-the-light-curves"]], "4.1 A single Classifier": [[6, "a-single-classifier"]], "4.1 Check that the data types are ok for sktime": [[6, "check-that-the-data-types-are-ok-for-sktime"]], "4.2 Loop over a bunch of classifiers": [[6, "loop-over-a-bunch-of-classifiers"]], "4a. Retrieve the HEASARC Catalog": [[2, "a-retrieve-the-heasarc-catalog"]], "4b. Run nway to do the Cross-Match": [[2, "b-run-nway-to-do-the-cross-match"]], "5) Wise bands alone": [[4, "wise-bands-alone"]], "5. Make plots of luminosity as a function of time": [[7, "make-plots-of-luminosity-as-a-function-of-time"]], "5. 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